Update README.md

README.md

@@ -2,9 +2,11 @@
 tags:
 - sentence-transformers
 - sentence-similarity
+- dense-encoder
+- dense
 - feature-extraction
 - telepix
-pipeline_tag: sentence-similarity
+pipeline_tag: feature-extraction
 library_name: sentence-transformers
 license: apache-2.0
 ---
@@ -13,11 +15,12 @@ license: apache-2.0
 <p>
 
 # PIXIE-Rune-Preview
-**PIXIE-Rune-Preview** is an encoder-based embedding model trained on Korean and English triplets, developed by [TelePIX Co., Ltd](https://telepix.net/). 
-**PIXIE** stands for Tele**PIX** **I**ntelligent **E**mbedding, representing TelePIX’s high-performance embedding technology. 
-The model is bilingual, specifically optimized for both Korean and English. 
-It demonstrates strong performance on retrieval tasks in both languages, achieving robust results across a wide range of Korean- and English-language benchmarks. 
-This makes it well-suited for real-world applications that require high-quality semantic search in Korean, English, or both.
+**PIXIE-Rune-Preview** is an encoder-based embedding model trained on Korean and English dataset, 
+developed by [TelePIX Co., Ltd](https://telepix.net/).
+**PIXIE** stands for Tele**PIX** **I**ntelligent **E**mbedding, representing TelePIX’s high-performance embedding technology.
+This model is specifically optimized for semantic retrieval tasks in Korean and English, and demonstrates strong performance in aerospace domain applications. Through extensive fine-tuning and domain-specific evaluation, PIXIE shows robust retrieval quality for real-world use cases such as document understanding, technical QA, and semantic search in aerospace and related high-precision fields.
+It also performs competitively across a wide range of open-domain Korean and English retrieval benchmarks, making it a versatile foundation for multilingual semantic search systems.
+
 
 ## Model Description
 - **Model Type:** Sentence Transformer
@@ -25,8 +28,8 @@ This makes it well-suited for real-world applications that require high-quality
 - **Maximum Sequence Length:** 8192 tokens
 - **Output Dimensionality:** 1024 dimensions
 - **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-- **Language:** Bilingual — optimized for high performance in Korean and English
+- **Language:** Multilingual — optimized for high performance in Korean and English
+- **Domain Specialization:** Aerospace semantic search
 - **License:** apache-2.0 
 
 ### Full Model Architecture
@@ -50,11 +53,12 @@ We report **Normalized Discounted Cumulative Gain (NDCG)** scores, which measure
 All evaluations were conducted using the open-source **[Korean-MTEB-Retrieval-Evaluators](https://github.com/BM-K/Korean-MTEB-Retrieval-Evaluators)** codebase to ensure consistent dataset handling, indexing, retrieval, and NDCG@k computation across models.
 
 #### 7 Datasets of MTEB (Korean)
-Our model, **telepix/PIXIE-Rune-Preview**, achieves state-of-the-art performance across most metrics and benchmarks, demonstrating strong generalization across domains such as multi-hop QA, long-document retrieval, public health, and e-commerce.
+Our model, **telepix/PIXIE-Rune-Preview**, achieves strong performance across most metrics and benchmarks, demonstrating strong generalization across domains such as multi-hop QA, long-document retrieval, public health, and e-commerce.
 
 | Model Name | # params | Avg. NDCG | NDCG@1 | NDCG@3 | NDCG@5 | NDCG@10 |
 |------|:---:|:---:|:---:|:---:|:---:|:---:|
 | **telepix/PIXIE-Rune-Preview** | 568M | **0.6905** | **0.6461** | **0.6859** | **0.7063** | **0.7238** |
+| telepix/PIXIE-Splade-Preview | 0.1B | 0.6677 | 0.6238 | 0.6628 | 0.6831 | 0.7009 |
 |  |  |  |  |  |  |  |
 | nlpai-lab/KURE-v1 | 568M | 0.6751 | 0.6277 | 0.6725 | 0.6907 | 0.7095 |
 | dragonekue/BGE-m3-ko | 568M | 0.6658 | 0.6225 | 0.6627 | 0.6795 | 0.6985 |
@@ -112,9 +116,7 @@ Descriptions of the benchmark datasets used for evaluation are as follows:
 - **SCIDOCS**  
   A citation-based document retrieval dataset focused on scientific papers.
   
-## Usage
-
-### Direct Usage (Sentence Transformers)
+## Direct Use (Semantic Search)
 
 First install the Sentence Transformers library: